W2 graded assignment - training the model fails although all previous tests pass

Course Q&A Natural Language Processing NLP with Sequence Models
1

C3W2 - error between exercise 5 and exercise 6.
When I am running cell below 4.4 - Training the Model, on some reason, the model fails to fit, and produces the following error (full text of error below). Since this cell does not have my own code and previous tests passed, I am quite lost on next steps.

I have assumed there was some error in Exercise 3 - NER model. I tried different combinations of parameters for tf.keras.layers.LSTM, but the initial variant I used seems to be the only correct one which passes the tests and further steps (I set return_sequences to True, and return_state to False).
Please advise what went wrong and where I can look for the source of error.

ValueError Traceback (most recent call last)
Cell In[102], line 5
1 tf.keras.utils.set_random_seed(33) ## Setting again a random seed to ensure reproducibility
3 BATCH_SIZE = 64
----> 5 model.fit(train_dataset.batch(BATCH_SIZE),
6 validation_data = val_dataset.batch(BATCH_SIZE),
7 shuffle=True,
8 epochs = 2)

File /usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.traceback)
68 # To get the full stack trace, call:
69 # tf.debugging.disable_traceback_filtering()
—> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb

File /tmp/autograph_generated_file_dita3ba.py:15, in outer_factory..inner_factory..tf__train_function(iterator)
13 try:
14 do_return = True
—> 15 retval = ag_.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False

ValueError: in user code:

File "/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py", line 1338, in train_function  *
    return step_function(self, iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py", line 1322, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py", line 1303, in run_step  **
    outputs = model.train_step(data)
File "/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py", line 1085, in train_step
    return self.compute_metrics(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py", line 1179, in compute_metrics
    self.compiled_metrics.update_state(y, y_pred, sample_weight)
File "/usr/local/lib/python3.8/dist-packages/keras/src/engine/compile_utils.py", line 605, in update_state
    metric_obj.update_state(y_t, y_p, sample_weight=mask)
File "/usr/local/lib/python3.8/dist-packages/keras/src/utils/metrics_utils.py", line 77, in decorated
    update_op = update_state_fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/src/metrics/base_metric.py", line 140, in update_state_fn
    return ag_update_state(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/src/metrics/base_metric.py", line 728, in update_state  **
    return super().update_state(matches, sample_weight=sample_weight)
File "/usr/local/lib/python3.8/dist-packages/keras/src/metrics/base_metric.py", line 504, in update_state
    ) = losses_utils.squeeze_or_expand_dimensions(
File "/usr/local/lib/python3.8/dist-packages/keras/src/utils/losses_utils.py", line 224, in squeeze_or_expand_dimensions
    sample_weight = tf.squeeze(sample_weight, [-1])

ValueError: Can not squeeze dim[1], expected a dimension of 1, got 104 for '{{node Squeeze}} = Squeeze[T=DT_FLOAT, squeeze_dims=[-1]](Cast_7)' with input shapes: [?,104].
2

Hi @Olga_Gnatenko

You might be having the same problem.

Cheers

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